Today we live in a world, where logistics has created winners, from Walmart to Amazon and many more. While over 4 billion people are connected by social media, almost all 7.8 billion have to be connected by logistics. Logistics today is everywhere, from moving essentials, products and vaccines to armies mobilizing for war.
But behind the scenes, a new logistics is rising; driven by data, powered by analytics, which helps companies serve their customers and countries to rise out of poverty, through reach and commerce-enabled at a speed and cost that were not possible even a decade ago.
Data is growing, and industry estimates suggest that over 1 lakh crores worth of transport spends data in India is now highly organized. It has reached a level of maturity and can enable the application of artificial intelligence and machine learning (AI & ML) to create the next level of value and savings. AI & ML married with logistics/ spend data is the next frontier for efficient supply chains, as was highlighted by Coupa’s USD 1.5 billion acquisition of Llamasoft, particularly when supply chains will need this intelligence to be agile and recover faster from disruptions like a pandemic.
This data coupled with multiple other data sources can be leveraged not only for individual companies but also in the broader public interest. One key example would be the creation of a national reefer grid spanning all available reefer capacity to enable efficient and safe distribution of the vaccine to 130 crore Indians, coordinating across manufacturers and transporters, as and when it is available. Similar platforms will need to be created around the world as the COVID vaccine becomes available, in the absence of which the vaccination process is likely to take much longer, with more wasted vaccine doses and longer wait times to be vaccinated.
While islands of excellence enabled by data do exist, the industry is still perennially struggling with issues such as information asymmetry, demand-supply gap, lack of transparency and poor infrastructure, and COVID-19 exacerbated these issues earlier this year. India has a higher industry spend on logistics at 13%+ of GDP as compared to developed countries such as the US and Germany. Poor physical and information infrastructure not only drives up costs and creates inefficiencies, but it also stifles a nation’s capacity to respond rapidly to a pandemic or natural disaster, the likes of which India faces every few years.
Current Issues and Their Ripple Effects on Other Sectors
Since the onset of the pandemic, the logistics sector has faced labour shortages, cargo capacity challenges, manufacturing slowdown, order delays, stuck shipments, etc. There have also been demand and supply shocks in the past few months, as the supply chains struggled through the pandemic.
These issues have an impact on other sectors, especially food and essentials. The UN estimates that more than 40% of food produced in India is wasted before it reaches the consumer. This wastage can be traced back to issues we have around supply chain bottlenecks, transport, and storage. There is not enough storage capacity and coupled with transport issues this leads to substantial losses of perishable items every year. The pandemic exacerbated these losses around the world, as produce rotted in the farms, with no farm-hands to harvest or logistics to transport it, especially when retail chains/stores had to shut down at the peak of the pandemic.
Hence, it has become essential to address the logistics industry’s challenges to continue the growth and survive shocks (pandemic, seasonal and otherwise).
Einstein once said, “no problem can be solved from the same level of consciousness that created it.” This applies perfectly to the logistics industry and data science could provide the much-needed solutions to remove these inefficiencies that have accumulated over the years.
Many of the fortune 500 companies have systems and databases that record their demand and forecast, while progressive transporters maintain a log of available vehicles with track and trace; what is missing though, is an integrative layer that combines the two at a network level to provide deeper and meaningful insights and results.
How Data Science Can Enable the Next Level for the Logistics Sector
1. Create an Integrated National Logistics Grid: The logistics sector in India spans more than 50,000 routes across 700+ districts, more than 10,000 manufacturing companies and 2 Lakh+ transporters, plying ~1 crore commercial vehicles. These large data sets exist largely in their silos with a manufacturer, transporter, or at location level with brokers/agents, but rarely in an integrated manner that would permit a network view.
Organizing the data into usable, interoperable matrices and integrating these into a national logistics grid will help create an information powerhouse that can be mined for analytics and insights and value creation opportunities. This first step itself will enable transparency and drive efficiencies by resolving information asymmetry and democratizing the logistics sector.
2. Make Logistics Infrastructure Smart, at Scale: Data science is also being used by countries to create smart infrastructure at a scale which can drive a new level of cost and value creation, especially in pandemic times when bottom-lines are stressed. Physical logistics infrastructure including roads and warehouses are being embedded with telematics/ sensors planned for the next level of driverless trucks & drones, with driverless trucks moving non-stop over long distances. Availability of real-time data & analytics to optimize the demand-supply patterns constantly at scale is becoming a reality and creating agile supply chains, which can be integrated into the national logistics grid.
3. Integrate Demand-Supply Patterns into the National Logistics Grid to Drive the Next Level of Efficiency & Visibility: Using the power of data and predictive analytics capabilities, we can plan more accurately, improve operational efficiency, automate processes and drive continuous innovation. Sophisticated demand forecasting algorithms can help manage production schedules to avoid customer impact. Uncertainty is one of the biggest challenges businesses face, and data science and predictive analytics can help businesses manage it much better. Historical data linked to seasonality, geographical variations and other variables, embedded in the National Logistics Grid, can be mined to reduce guesswork and ease out stress on the network in advance ensuring harmony in logistics flows.
Machine learning can also help integrate otherwise disconnected points in the network to assess interdependencies at a network level and use changes in leading variables to predict the impact on dependent activities and outcomes. Weather data, civil unrest, or currency fluctuations in one part of the logistics network can have a multiplier effect on another part. Technology can help forecast and mitigate some of this risk. Access to better data helps in greater automation, easier deployment of IoT and better collaboration among companies. IoT combined with big data can get real-time information about delivery and goods for manufacturers and shippers as many e-commerce companies have already been providing.
Collaboration across companies and channels can also improve load optimization and hence drive efficiency. Advances in mobile technology have the potential to help logistics through functions such as bar-code reading, route optimization, asset tracking and warehouse management. Access to better data also provides a better audit of the supply chain to plug leakages and inefficiencies.
Organizing the logistics industry has been the dream of many for an upstart, and governments have made earnest attempts. But like a black swan, perhaps this industry will rapidly organize in unprecedented ways, as multiple powerful forces for change are now aligning, starting with the Goods & Service Tax (GST) reform which gave us a single national market, to the electronic e-way bill, electronic tolls and multiple other initiatives, all with a common denominator: they all move the logistics industry to digitize and enable transparency while building the foundation for leveraging data science.
We may still be some distance from having an organized logistics industry, but the process is well underway, and getting faster. The tipping point is now in sight. We may not be there yet, but the speed at which Indian logistics will move may surprise all of us in the coming years.